Pulmonary Textures Classification via a Multi-Scale Attention Network
| Autor: | Tomoko Gyobu, Osamu Honda, Yasushi Hirano, Xinchen Ye, Rui Xu, Noriyuki Tomiyama, Shoji Kido, Yutaka Kawata, Cong Zhen |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Lung Diseases
Computer science media_common.quotation_subject CAD 02 engineering and technology Solid modeling 030218 nuclear medicine & medical imaging 03 medical and health sciences Deep Learning 0302 clinical medicine Health Information Management Discriminative model Image Interpretation Computer-Assisted Image Processing Computer-Assisted 0202 electrical engineering electronic engineering information engineering Humans Electrical and Electronic Engineering Lung media_common Network architecture Creative visualization business.industry Deep learning Pattern recognition Computer Science Applications Visualization Feature (computer vision) 020201 artificial intelligence & image processing Artificial intelligence Tomography X-Ray Computed business Biotechnology |
| Zdroj: | IEEE Journal of Biomedical and Health Informatics. 24:2041-2052 |
| ISSN: | 2168-2208 2168-2194 |
| Popis: | Precise classification of pulmonary textures is crucial to develop a computer aided diagnosis (CAD) system of diffuse lung diseases (DLDs). Although deep learning techniques have been applied to this task, the classification performance is not satisfied for clinical requirements, since commonly-used deep networks built by stacking convolutional blocks are not able to learn discriminative feature representation to distinguish complex pulmonary textures. For addressing this problem, we design a multi-scale attention network (MSAN) architecture comprised by several stacked residual attention modules followed by a multi-scale fusion module. Our deep network can not only exploit powerful information on different scales but also automatically select optimal features for more discriminative feature representation. Besides, we develop visualization techniques to make the proposed deep model transparent for humans. The proposed method is evaluated by using a large dataset. Experimental results show that our method has achieved the average classification accuracy of 94.78% and the average f-value of 0.9475 in the classification of 7 categories of pulmonary textures. Besides, visualization results intuitively explain the working behavior of the deep network. The proposed method has achieved the state-of-the-art performance to classify pulmonary textures on high resolution CT images. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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